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\begin{document}

\title{May EFZ help Big Data Analytics!}
\author{\IEEEauthorblockN{\large Hassan Eslami, Faraz Faghri and Xin Zhao\IEEEauthorrefmark{1}}
    \\[-2.0ex]
  \IEEEauthorblockA{\IEEEauthorrefmark{1}University of Illinois at
    Urbana-Champaign, \{eslami2,faghri2,xinzhao3\}@illinois.edu}}

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\begin{abstract}
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Big data is inherently an IO intensive problem while many
analytical applications are computation intensive which makes
big data analytics both IO and computation intensive. Majority
of proposed solutions target each of these requirements individually, a class of these systems deploy computation close to
storage to overcome the big data challenge and some utilize
various parallel techniques to improve the processing time.

Combining goods of both worlds, parallel computing and distributed systems, has been a long lived challenge. Complexity and usability has been a problem with many proposed systems. We beleive and an application specific solutions is the answer to this problem. We will select some of the highly used analytical algorithms and will build application specific solutions for them. This approach should help us mask many complexity and usability issues from the end-user. 

\end{abstract}

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